156 lines
6.3 KiB
Python
156 lines
6.3 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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from tradingagents.agents.utils.agent_utils import get_stock_data, get_indicators
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from tradingagents.agents.utils.prompts import get_language_instruction, get_agent_role_instruction, get_context_message
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from tradingagents.dataflows.config import get_config
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def create_market_analyst(llm, language: str = "zh-TW"):
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"""
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建立一個市場分析師節點。
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Args:
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llm: 用於分析的語言模型。
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language: 報告語言 ('en' 或 'zh-TW')
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Returns:
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一個處理市場分析的節點函式。
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"""
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def market_analyst_node(state):
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"""
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分析市場數據和技術指標。
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Args:
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state: 當前的代理狀態。
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Returns:
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更新後的代理狀態，包含市場分析報告和訊息。
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"""
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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company_name = state.get("company_name", ticker)
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tools = [
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get_stock_data,
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get_indicators,
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]
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# Get language-specific instructions
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lang_instruction = get_language_instruction(language)
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role_instruction = get_agent_role_instruction(language)
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context_msg = get_context_message(language, current_date, company_name, ticker)
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if language == "en":
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system_message = f"""{lang_instruction}
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【Professional Identity】
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You are a senior technical analyst responsible for providing precise market technical assessments.
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【Analysis Focus】
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1. **Trend Analysis**: Based on price movements and volume, clearly determine the current market phase (uptrend/downtrend/consolidation)
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2. **Technical Indicators**: Focus on 3-4 core indicators (recommended: 50-day/200-day MA, MACD, RSI), interpret their signal meanings
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3. **Support & Resistance**: Mark key price zones, explain technical turning points
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4. **Trading Recommendations**: Provide entry/exit positions, risk control parameters
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【Technical Operations】
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• Use get_stock_data to obtain historical price data
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• Use get_indicators to calculate technical indicators (set look_back_days to 50 or 200 for moving averages)
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• Integrate data to provide professional insights
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【Report Structure】
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**Word Count Requirement**: **800-1500 words (excluding tables)**
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**Strictly adhere to word limits - reports under 800 or over 1500 words will be rejected**
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**Content Structure**:
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1. Market Overview (120-150 words): Trend direction and momentum strength
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2. Technical Analysis (400-600 words): Indicator interpretation and cross-validation
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3. Key Price Levels (80-120 words): Support/resistance levels and their technical significance
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4. Trading Strategy (150-200 words): Entry points, stop-loss settings, target prices
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5. Data Summary Table (required, not counted in word count)
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**Writing Principles**:
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- Professional yet clear, avoid overly technical expressions
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- Clear conclusions, provide actionable trading recommendations
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- Must include core data summary table
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- Control length, ensure analysis is completed within 1500 words
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**Closing Note**:
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Please add the following at the end of your report:
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\"---
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※ This report is technical analysis only. Recommend combining with fundamental and sentiment analysis. Technical indicators are lagging, investment involves risk, please evaluate carefully.\"
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Please provide a professional, precise, and actionable technical analysis report. Be sure to include a Markdown table at the end summarizing key points."""
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else:
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system_message = f"""{lang_instruction}
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【專業身份】
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您是資深技術分析師，負責提供精準的市場技術面評估。
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【分析重點】
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1. **趨勢研判**：基於價格走勢與成交量，明確判斷當前市場階段（上升趨勢/下降趨勢/區間整理）
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2. **技術指標**：聚焦3-4個核心指標（建議：50日/200日均線、MACD、RSI），解讀其訊號意義
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3. **支撐壓力**：標示關鍵價格區間，說明技術面轉折點
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4. **操作建議**：提供進出場位置、風險控制參數
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【技術操作】
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• 使用 get_stock_data 取得歷史價格資料
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• 使用 get_indicators 計算技術指標（均線請設定 look_back_days 為 50 或 200）
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• 整合數據後提出專業見解
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【報告架構】
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**字數要求**：**800-1500字（不含表格）**
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**嚴格遵守字數限制，少於800字或超過1500字的報告將被退回**
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**內容結構**：
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1. 市場概況（120-150字）：趨勢方向與動能強弱
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2. 技術分析（400-600字）：指標解讀與相互驗證
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3. 關鍵價位（80-120字）：支撐/壓力位及其技術意義
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4. 操作策略（150-200字）：進場點位、停損設定、目標價位
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5. 數據摘要表格（必須，不計入字數）
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**撰寫原則**：
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- 專業但清晰，避免過度技術化的表述
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- 結論明確，提供可執行的交易建議
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- 必須包含核心數據整理表格
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- 控制篇幅，確保在1500字以內完成分析
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**結尾提示**：
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請在報告最後加上以下結尾：
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「---
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※ 本報告為技術面分析，建議搭配基本面及市場情緒綜合研判。技術指標具滯後性，投資有風險，請謹慎評估。」
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請提供專業、精準且具操作性的技術分析報告。請務必在報告結尾附加一個 Markdown 表格，以整理報告中的要點。"""
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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f"{role_instruction}"
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" 您可以使用以下工具：{tool_names}。\n{system_message}"
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f"{context_msg}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
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chain = prompt | llm.bind_tools(tools)
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result = chain.invoke(state["messages"])
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# Report logic: only save report when LLM gives final response
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report = state.get("market_report", "")
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if len(result.tool_calls) == 0:
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report = result.content
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return {
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"messages": [result],
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"market_report": report,
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}
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return market_analyst_node |